System and Method for Non-Invasive Tissue Characterization

ABSTRACT

Disclosed herein is a non-invasive system for determining tissue composition. The system comprises an imaging system with a non-invasive probe, a signal analyzer, and a correlation processor. The probe includes active imaging components for emitting energy and collecting imaging data including reflected signals from an object of interest. The signal analyzer analyzes the imaging data and determines one or more signal properties from the reflected signals. The correlation processor then associates the one or more signal properties to pre-determined tissue signal properties of different tissue components through a pattern recognition technique wherein the pre-determined tissue signal properties are embodied in a database, and identifies a tissue component of the object based on the pattern recognition technique.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to and the benefit of U.S.Provisional Patent Application No. 61/733,738, filed Dec. 5, 2012, whichis hereby incorporated by reference in its entirety.

BACKGROUND

Imaging various tissues and organs of the human body provides usefulinformation in various disciplines of medical practice for determiningthe best type and course of treatment. For example, intravascularimaging technologies have enabled doctors to create and view a varietyof images generated by an imaging device inserted within vasculature.Imaging of the coronary vessels of a patient by techniques involvingintravascular insertion of a catheter-mounted probe (e.g., an ultrasoundtransducer array) can provide physicians with valuable information. Suchimage data indicates the extent of a stenosis in a patient, revealsprogression of disease, and helps determine whether procedures such asangioplasty or atherectomy are indicated or whether more invasiveprocedures are warranted.

The development of new imaging and/or examining technologies hasprovided an increasing number of options available to doctors for thenon-invasive diagnosis and evaluation of disease. Medical technologiesfor externally imaging and/or examining both external and internalbodily structures offers a diagnostic tool to establish the need fortreatment of a diseased structure, to determine the most appropriatecourse of treatment, and to assess the effectiveness of the treatment.Such external imaging and/or evaluation techniques can complementtraditional radiological imaging techniques (e.g., angiography andmammography) by providing images of the tissue and/or fluid flow withoutintroducing instruments (and the associated health risks) into thepatient's body. Internal body structures may be imaged and/or examinedto determine the structural or flow characteristics that may indicateabnormalities such as, but not limited to, tumors, cysts, abscesses,mineral deposits, obstructions, plaques, and other anatomical defects orpathologies. Often these high quality images are generated insubstantially real time. However, analysis of these images oftenlocalizes the object of interest but does not characterize the tissue,requiring a biopsy to classify the tissue and establish a definitivediagnosis. Also, analysis of these images are dependent upon highlytrained observers and may be subject to observer biases and result inknown observer-variability.

The devices, systems, and methods disclosed herein overcome one or moreof the deficiencies of the prior art.

SUMMARY

The present disclosure provides devices, systems, and methods fordetermining the type(s) of tissue present within a patient utilizing anon-invasive, external imaging probe. The resulting determination of thetissue type(s) can be utilized to determine an appropriate, customizedtreatment plan for the patient, leading to improved patient outcomes.

The present disclosure relates generally to non-invasive tissuecharacterization within an animal, including human bodies. In oneaspect, the present disclosure provides a system for determining tissuecomposition based on signals received from a non-invasive probe. Thesystem includes a signal analyzer for analyzing the imaging data anddetermining one or more signal properties from the reflected signalsalong with a correlation processor configured to associate the one ormore signal properties to pre-determined tissue signal properties ofdifferent tissue components through a pattern recognition technique. Thepre-determined tissue signal properties can be embodied in a database,and the correlation processor is further configured to identify a tissuecomponent based on the pattern recognition technique. In one alternativeform, the imaging system collects imaging data from more than oneimaging modality, and the signal analyzer is configured to analyze morethan one type of imaging data to determine signal properties associatedwith each imaging modality. In this alternative form, the correlationprocessor is configured to identify a tissue component of the objectbased on associating the signal properties to pre-determined tissuesignal properties of different tissue components for the differentimaging modalities.

In another aspect, the disclosure provides an article of manufactureembodied in a computer-readable medium for use in a processing systemfor analyzing imaging signal data associated with a non-invasive imagingprobe. The article of manufacture comprising first processor executableinstructions for causing a processor to receive imaging signal data of ascanned object collected from a non-invasive probe; second processorexecutable instructions for causing the processor to determine signalproperties of one or more regions of interest associated with thescanned object from the image signal data; and third processorexecutable instructions for causing the processor to classify the one ormore regions of interest as a tissue component type based on aclassification data structure pre-determined from measured associationsbetween signal properties, secondary parameters, and one or more tissuecomponent types of an object similar to the scanned object.

In still a further aspect, the present disclosure provides a method ofidentifying one or more tissue components of a scanned object of apatient. The method comprising receiving reflected signals from anon-invasive probe scanning the object from a location external to theobject; determining one or more signal properties from the reflectedsignals; associating the one or more signal properties to pre-determinedsignal properties of tissue components of an object similar to thescanned object wherein the pre-determined signal properties compriseclassification conditions stored in a data structure; and identifyingone or more tissue components based on the associating.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory innature and are intended to provide an understanding of the presentdisclosure without limiting the scope of the present disclosure. In thatregard, additional aspects, features, and advantages of the presentdisclosure will be apparent to one skilled in the art from the followingdetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate embodiments of the devices andmethods disclosed herein and together with the description, serve toexplain the principles of the present disclosure. Throughout thisdescription, like elements, in whatever embodiment described, refer tocommon elements wherever referred to and referenced by the samereference number. The characteristics, attributes, functions,interrelations ascribed to a particular element in one location apply tothose elements when referred to by the same reference number in anotherlocation unless specifically stated otherwise.

The figures referenced below are drawn for ease of explanation of thebasic teachings of the present disclosure only; the extensions of thefigures with respect to number, position, relationship and dimensions ofthe parts to form the preferred embodiment will be explained or will bewithin the skill of the art after the following description has beenread and understood. Further, the exact dimensions and dimensionalproportions to conform to specific force, weight, strength and similarrequirements will likewise be within the skill of the art after thefollowing description has been read and understood.

The following is a brief description of each figure used to describe thepresent invention, and thus, is being presented for illustrativepurposes only and should not be limitative of the scope of the presentinvention.

FIG. 1 is a simplified block diagram of the individual components of anexemplary external imaging system, including a probe, according to oneembodiment of the present disclosure.

FIGS. 2 a and 2 b are illustrations of an imaging probe (a) scanning anobject and (b) receiving reflected signals from the object according toone embodiment of the present disclosure.

FIGS. 3 a-3 d are illustrations of an imaging probe scanning an objectat different angles of incidence relative to the object according to oneembodiment of the present disclosure.

FIG. 4 is a simplified block diagram of individual components of anexemplary external imaging system according to one embodiment of thepresent disclosure.

FIG. 5 is an illustration of an ultrasound probe scanning an objectaccording to one embodiment of the present disclosure.

FIG. 6 is an illustration of an ultrasonic A-scan according to oneembodiment of the present disclosure.

FIG. 7 is an illustration of a power spectrum plot generated from theA-scan signal shown in FIG. 6.

FIG. 8 is a simplified block diagram of a signal analyzer systemaccording to one embodiment of the present disclosure.

FIG. 9 is an illustration of a classification tree of spectralproperties in accordance with the principles of the present disclosure.

FIG. 10 is a simplified flow diagram illustrating a methodology forcharacterizing tissue components using the imaging system shown in FIG.1 according to one embodiment of the present disclosure.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the embodimentsillustrated in the drawings, and specific language will be used todescribe the same. It will nevertheless be understood that no limitationof the scope of the disclosure is intended. Any alterations and furthermodifications to the described devices, instruments, methods, and anyfurther application of the principles of the present disclosure arefully contemplated as would normally occur to one skilled in the art towhich the disclosure relates. In particular, it is fully contemplatedthat the features, components, and/or steps described with respect toone embodiment may be combined with the features, components, and/orsteps described with respect to other embodiments of the presentdisclosure. For simplicity, in some instances the same reference numbersare used throughout the drawings to refer to the same or like parts.

The present disclosure describes systems and methods for tissuecharacterization by analyzing images created by an energy emissiondevice, such as, by way of non-limiting example, an ultrasoundtransducer, deployable with an imaging system to facilitateinterpretation of images of a patient's tissues of interest, such as ablood vessel or tumor. The systems and methods described hereincorrelate image properties of the tissues of interest withpre-determined tissue properties to automatically and reproduciblycharacterize the tissues of interest in real time (i.e., as the tissuesare being imaged). In some embodiments, the systems and methodsdescribed herein utilize various parameters related to the angle ofincidence of the imaging modality, particular anatomical characteristicsof the patient, and/or medical conditions of the patient to bettercharacterize the tissues of interest. In particular, the disclosuredescribes one embodiment that performs tissue characterization of anintravascular plaque using images obtained using an external ultrasonicdevice. By automatically and reproducibly characterizing the imagedtissues in real time, the systems and methods described herein minimizethe known observer-variability associated with tissue characterizationby observers. Moreover, by specifically characterizing tissue types, thesystems and methods described herein reduce the need for post-imagingbiopsies before initiating treatment, thereby accelerating the onset oftreatment for the patient.

It should be appreciated that while the exemplary embodiment isdescribed in terms of an ultrasonic device, or more particularly the useof ultrasound data (or a transformation thereof) to render images of abodily structure or object, the present disclosure is not so limited.Thus, for example, using backscattered data (or a transformationthereof) based on spectroscopy or even electromagnetic radiation (e.g.,light waves in non-visible ranges such as used in OCT, X-Ray CT, etc.)to render images and/or imaging data of any tissue type or composition(not limited to vasculature, but including other structures both withinand on the surface of a patient, including human as well as non-humanpatients) is within the spirit and scope of the present disclosure. Anyform of external imaging, measuring, and/or evaluation device (andresultant data) is within the spirit and scope of the presentdisclosure.

The following includes definitions of selected terms used throughout thedisclosure. Both singular and plural forms of all terms fall within eachmeaning.

“Computer-readable medium,” as used herein, refers to any medium thatparticipates in directly or indirectly providing signals, instructionsand/or data to one or more processors for execution. Such a medium maytake many forms, including but not limited to, non-volatile media,volatile media, and transmission media. Non-volatile media may include,for example, optical or magnetic disks. Volatile media may includedynamic memory. Transmission media may include coaxial cables, copperwire, and fiber optic cables. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications, or take the form of one or moregroups of signals. Common forms of computer-readable media include, forexample, a floppy disk, a flexible disk, hard disk, magnetic tape, orany other magnetic medium, a CD-ROM, any other optical medium, punchcards, papertape, any other physical medium with patterns of holes, aRAM, a PROM, an EPROM, a FLASH-EPROM, any other memory chip orcartridge, a carrier wave/pulse, or any other medium from which acomputer, processor or other electronic device can read.

“Logic,” as used herein, includes, but is not limited to, hardware,firmware, software and/or combinations of each to perform a function(s)or an action(s), and/or to cause a function or action from anothercomponent. For example, based on a desired application or needs, logicmay include a software controlled microprocessor, discrete logic such asan application specific integrated circuit (ASIC), a programmed logicdevice, memory device containing instructions, or the like. Logic mayalso be fully embodied as software.

“Patient,” as used herein, includes, but is not limited to, a livinganimal including humans and non-humans.

“Signal,” as used herein, includes, but is not limited to, one or moreelectrical signals, analog or digital signals, one or more computer orprocessor instructions, messages, a bit or bit stream, or other meansthat can be received, transmitted, and/or detected.

“Software,” as used herein, includes, but is not limited to, one or morecomputer readable and/or executable instructions that cause a computeror other electronic device to perform functions, actions, and/or behavein a desired manner. The instructions may be embodied in various formssuch as routines, algorithms, modules or programs including separateapplications or code from dynamically linked libraries. Software mayalso be implemented in various forms such as a stand-alone program, afunction call, a servlet, an applet, instructions stored in memory, partof an operating system or other type of executable instructions. It willbe appreciated by one of ordinary skill in the art that the form ofsoftware is dependent on, for example, requirements of a desiredapplication, the environment it runs on, and/or the desires of adesigner/programmer or the like.

“User,” as used herein, includes, but is not limited to, one or morepersons, software, computers or other devices, or combinations of these.

With reference to the figures, FIG. 1 illustrates one embodiment of atissue characterization system 100 configured to non-invasively scan anobject 105 and analyze one or more characteristics of the object 105,which may be located on the external surface or inside a patient's body.In the pictured embodiment, the system 100 includes an imaging systemconsole 110 that includes data processing, analysis, and/or displaycapabilities. The imaging console 110 may be a general purpose computerconfigured to communicate with and collect data from an imaging probe115. In another embodiment, the imaging console 110 may be, for example,a General Electric Vivid 5 Echocardiography System, a Hewlett-PackardSONOS System, or other types of ultrasound systems. In anotherembodiment, the console 110 may be a small portable scanner. In thepictured embodiment, the imaging probe 115 is a non-invasive deviceconfigured to scan the object 105 from a location external to apatient's body. As used herein, the term “non-invasive” refers toprocedures in which no break in the skin surface is created. As such,the term “non-invasive” encompasses procedures extending through naturalbody orifices, such as, by way of non-limiting example, the mouth, thenasal cavity, the ear canal, esophagus, or the rectum. Various types ofprobes can be used, such as, by way of non-limiting example, aphased-array ultrasound probe, an OCT probe, a spectroscopy probe, amulti-modality or combination probe (incorporating various imagingmodalities), a linear probe, a curvilinear probe, or other types ofhand-held probes.

As shown in FIG. 2 a, to perform a scan, the probe 115 may be placedagainst or near the patient's skin S near the object or region ofinterest 105. The non-invasive probe 115 is shaped and configured foruse against and/or near the external skin surface S of a patient. Inthat regard, the shape and configuration of the probe 115 illustratedherein is for exemplary purposes only and in no way limits the manner inwhich the probe 115 may be shaped in other embodiments. The probe caninclude a housing 116 adapted for gripping by a user and have acommunication connection 118 to a console (not shown). Alternatively,the communication connection can be a wireless connection. Generally,the probe 115 may be configured to take on any desired profile, whichmay depend upon the type of imaging probe (e.g., ultrasound, OCT,multi-modality, etc.), the desired application, or the particular tissueof interest. In some embodiments, aspects of the probe 115 may besubstantially similar to aspects of an ultrasound probe disclosed inU.S. Pat. No. 6,837,853, titled “System and Method for Using anUltrasound Transducer with an Integrated Transducer Information System,”which is incorporated by reference herein in its entirety. In someembodiments, aspects of the probe 115 may be substantially similar toaspects of an ultrasonic probe disclosed in U.S. Patent Application No.2011/0125025, titled “Three Dimensional Imaging Ultrasound Probe,” whichis incorporated by reference herein in its entirety.

The active imaging components 120 (e.g., the transducers of anultrasound probe) of the probe 115 can be activated along differentpaths or scan lines 122 directed at the object or region of interest andemit energy along those paths. Although the active imaging components120 are arranged linearly so as to create a parallel array of paths 122in the pictured embodiment, in other embodiments, the active imagingcomponents may be positioned on the probe 115 in any of a variety ofarrangements. Thus, in other embodiments, the active imaging componentsmay be arranged so as to create a nonparallel array of energy emissionpaths. In the pictured embodiment, the imaging probe 115 is positionedsubstantially parallel to a longitudinal axis LA of the object, suchthat the emitted energy contacts the object 105 at an angle of incidenceA that is substantially ninety degrees relative to the longitudinal axisLA of the object 105. In other embodiments, as indicated below in FIGS.3 a-3 c, the imaging probe may be positioned at any of a variety ofangles relative to the object 105 so as to enable the emitted energy tointeract with the object 105 at a variety of angles of incidence A.

In some embodiments, as shown in FIG. 3 d, the active imaging components120 may emit energy at a variety of angles of incidence A although theimaging probe 115 itself remains positioned relatively parallel to thelongitudinal axis LA of the object 105. In FIG. 3 d, the active imagingcomponent 120 a emits energy (e.g., ultrasound waves) toward the object105 at an angle of incidence A1, and the active imaging component 120 bemits energy at an angle of incidence A2. In one embodiment, the energyemitted by different active imaging components intersects andsimultaneously interact with the object 105, resulting in acharacteristic pattern of reflected energy. In one aspect, non-parallelemitters may be activated substantially simultaneously. In one aspect,non-parallel emitters may be activated sequentially. In another aspect,non-parallel emitters operate at different frequencies.

As shown in FIG. 2 b, the active imaging components 120 then acquiredata reflected from the tissue along each path 122. Different types anddensities of tissue absorb and reflect emitted energy differently.Tissues that receive the emitted energy reflect and transmit some of theenergy as reflected signals 125. The reflected signals 125 are thenreceived by the active imaging components 120 in the probe 115. Thereflected signals 125 are shown as parallel signals for illustrativepurposes only. In actuality, the reflected signals 125 may approach tothe probe 115 at any of a variety of angles. In some embodiments, theactive imaging components comprise combined energy emitting and energyreceiving components (i.e., individual components capable of performingboth functions). In other embodiments, the active imaging componentscomprise separate energy emitting and energy receiving components. Thedifference between the energy signals transmitted and received by theprobe 115 is that the received signal 125 is the attenuated (andpossibly backscattered) version of the transmitted signal.

Each reflected signal 125 is characteristic of the type of tissues thatreflected it. Differences in the reflected signal 125 along each pathcan be determined by performing analysis on the signals. As a result,identifying different signal characteristics along each path 122 allowsfor a correlation to the type of tissue associated with those particularsignal characteristics. As will be described below, the signalcharacteristics of each reflected signal 125 can serve as a signaturefor different types of components within the object 118, including, forexample, plaque components within an artery or malignant cell typeswithin a tumor.

FIGS. 3 a-3 d illustrate the non-invasive imaging probe 115 scanning theobject 105 at different angles of incidence relative to the longitudinalaxis LA of the object 105 according to one embodiment of the presentdisclosure. In FIG. 3 a, the probe 115 is positioned at an angle abovethe skin surface S, and the emitted energy interacts with the object 105at an acute angle of incidence A (relative to the longitudinal axis LAof the object 105). In FIG. 3 b, the probe 115 is positioned pushingagainst the skin surface S at an angle relative to the object 105, andthe emitted energy interacts with the object 105 at an acute angle ofincidence A (relative to the longitudinal axis LA of the object 105). InFIG. 3 c, the probe 115 is positioned at an angle above the skin surfaceS, and the emitted energy interacts with the object 105 at an obtuseangle of incidence A (relative to the longitudinal axis LA of the object105).

The reflective signals 125 obtained from the object 105 will typicallybe characteristic of the angle of incidence A of the emitted energy. Forexample, a series of reflective signal data sets obtained at differentangles of incidence can help distinguish between isotropic andanisotropic tissues. For example, isotropic tissues (e.g., fat)typically have identical properties in all directions, while anisotropictissues (e.g., muscle fibers) have directionally dependent properties.In some instances, different types of tumors and mass lesions may havecharacteristic anisotropic properties. Isotropic and anisotropic tissueshave different acoustic and/or optical impedances and properties whenthe source of the imaging signals are orthogonal or at other angles tothe tissue. For example, if all the data sets of reflected signals fromthe object 105 reveal the same information, regardless of the angle ofincidence associated with each set, the imaging system 100 may bealerted to the presence of isotropic tissue. In contrast, if the datasets of reflected signals from the object 105 reveal differentinformation, the imaging system 100 may be alerted to the presence ofanisotropic tissue (and, in some instances, a particular type ofanisotropic tissue). In some instances, the user may reposition theprobe 115 at a series of different angles around the object 105 so as toobtain a series of sets of reflective signals 125, wherein each set isassociated with a different angle of incidence A relative to the object105. In some instances, such as where the active imaging components 120of the probe 115 are moveable or otherwise capable of emitting energy ata variety of angles, the user may obtain such a series of sets ofreflective signals 125 without repositioning the probe 115.

Referencing FIG. 1, the data collected by the probe 115 is initially inthe form of raw data 130 of the reflected signals 125 along each scanline. The data 130 is then refined or transformed into a format that canbe analyzed to determine various signal characteristics that mayidentify associated tissue types within and adjacent to the scannedobject 105. A signal analyzer logic 135 is configured to process andanalyze the data 130 to identify, in real-time, the various componentsof the scanned object 105. The logic 135 is configured to identifyvarious types of tissue and/or cells and to provide an assessment as tothe patient's condition based on the type of tissues and/or cellsidentified, an amount of tissue and/or cells identified, or both.

The signal analyzer logic 135 includes logic to transform the data 130into an analyzable domain and analyze the transformed information fromthe signals to determine one or more signal properties 140. For example,each scan line can be analyzed in segments and signal properties can bedetermined for each segment. The segments may be equal in size,different in size, equally spaced from each other, overlapping eachother, and/or defined in other desired ways.

The signal properties 140 are processed by correlation logic 145configured to correlate the signal properties of the scan line segmentwith the type of tissue component having those or similar signalproperties. In that regard, the correlation logic 145 is configured tocompare and match the signal properties 140 to pre-determined orpre-generated tissue signal properties 150 contained within a database155. Various parameters may comprise the database of pre-determinedtissue signal properties 150. The parameters comprising the database 155would be pertinent to both the desired application or tissue-of-interestand the imaging modality of the imaging probe (i.e., ultrasound, OCT,spectroscopy, etc.). In addition, the database can contain otherinformation or ‘macro’ data. These could be: (a) patient demographicsrelated (age, gender, race) or (b) patient health-specific (diabetes,history of hypertension, arthritis, or other related criteria dependingon the tissue being evaluated), or (c) more related to the tissue beingevaluated (size and location of lesion in the coronary tree—close to abifurcation or not, restricting blood flow or not, etc.). Thecorrelation logic 145 is configured to recognize the type of imagingmodality employed by the imaging probe 115 and to use the appropriatepre-determined tissue signal properties 150 associated with thatparticular imaging modality. For example, if the imaging modality beingused were ultrasound, the pre-determined signal properties 150 mayinclude various parameters in the spectral domain directly associatedwith scatter size, density, viscosity, and their acoustic propertiessuch as impedance and attenuation coefficient.

In some embodiments, the imaging system 100 may employ a multitude ofdifferent imaging modalities to image the same object 105. In someembodiments, these imaging modalities are used sequentially, whereas inother embodiments, the different imaging modalities are usedsimultaneously (e.g., using a multi-modality imaging probe 115). In oneembodiment, the imaging probe 115 may be configured to image the object105 using a multitude of different imaging modalities (e.g., OCT andultrasound). In some embodiments, the correlation logic 145 isconfigured to combine or analyze the pre-determined signal properties150 associated with each imaging modality used to perform the tissuecharacterization 165.

Secondary parameters 160 may be included within the data structure toreflect the non-invasive nature of the imaging and/or the particularpre-existing conditions or differential diagnoses of the patient. Thesecondary parameters 160 may be utilized by the correlation logic tomore accurately compare and match the signal properties 140 to thepre-determined signal properties 150. One secondary parameter 160associated with the non-invasive nature of the imaging may comprise thepatient's body-mass index to give an indication of the amount of muscle,fat, and other tissue that may lie in the path of the energy emitted bythe probe, which may affect the attenuation of the energy emitted towardand reflected from the object or region of interest.

Another secondary parameter 160 associated with the non-invasive natureof the imaging may comprise the angle of incidence of the imaging probe(or, more precisely, the emitted energy) relative to the object 105. Insome embodiments, the imaging system 100 can determine the angle ofincidence A of the emitted energy relative to the longitudinal axis LAof the object 105 and use this as a secondary parameter beforeautomatically selecting the appropriate pre-determined signal propertiesassociated with that angle of incidence or appropriately adjusting thepre-determined signal properties 150 to reflect this angle of incidence.In other embodiments, the user may enter the angle of incidence Amanually (e.g., via a graphical user interface attached to the imagingconsole 110), and either the user or the imaging system 100 may selectthe appropriate pre-determined signal properties 150 associated withthat angle of incidence or appropriately adjust the pre-determinedsignal properties to reflect this angle of incidence. In someembodiments, a three-dimensional data set can be constructed with theimaging probe 115 to provide further parameters related to tissueisotropy and/or anisotropy and matched back to the secondary parameters160 in the database 155 that contains these pre-determined values atvarious angles of incidence.

Another secondary parameter 160 associated with the imaging may comprisethe particular frequency or harmonics employed by the imaging probe 115.For example, the database 155 may contain particular sets ofpre-determined signal properties 150 associated with particularfrequencies or harmonic patterns.

Other secondary parameters 160 included within the database ofpre-determined signal properties may relate to the particularpre-existing conditions or differential diagnoses of the patient. Forexample, in the context of imaging atherosclerosis non-invasively, itmight be important to relate whether a patient is diabetic and/orhypertensive to better analyze the signal properties and to give anappropriate disease risk level. The secondary parameters 160 may beentered into the database 155 by the user or be selected from apre-established menu or list of options already present within thedatabase.

In one embodiment, the pre-determined signal properties 150 andsecondary parameters 160 discussed above are configured in the database155 or data structure that associates measured or observed signalproperties 140 to pre-determined tissue signal properties 150 thatreflect specific types of tissue component such as, by way ofnon-limiting example, fluid, blood, normal tissue, necrotic tissue,benign tumor tissue, or malignant tumor tissue. The data structure 155may be implemented in a variety of ways including a data file, an array,a table, a linked list, a tree structure, a database, neural network,combinations of these and multiple components of each if desired. Thecorrelation logic 145 matches the signal properties 140 from a path orscan line, or a region of the path or scan line, to the pre-determinedproperties 150 and outputs a tissue characterization 165 that identifiesthe type of tissue (and/or cell). In some embodiments, the correlationlogic 145 weights the pre-determined properties with other parameters(such as body-mass index) before matching the signal properties 140 froma path or scan line, or a region of the path or scan line, to theweighted pre-determined properties 150 and outputting a tissuecharacterization 165. The system 100 then repeats the analysis for othersegments on the current path or scan line and then for the other pathsor scan lines. The analysis can be performed along a single path in onedimension, multiple paths within the same plane can be analyzed for twodimensional considerations, or a series of scans forming a threedimensional volume can be analyzed. In some embodiments, the system 100may repeat the analysis for the imaging data received by each of theother imaging modalities. In some such embodiments, the system 100utilizes the correlation logic 145 to combine the correlationconclusions from different imaging modalities to arrive at a finaltissue characterization 165.

With reference to FIG. 4, once a sufficient amount of data is analyzedand the tissue types of the object 105 are characterized, a diagnosticlogic 200 may be included in the imaging system 100 to generate anassessment 205 as to the type and amount of different tissues identifiedand a health condition of the patient in light of the patient'spreexisting health conditions, symptoms, and differential diagnosis.Additionally, the diagnostic logic 200 may be configured to reconstructthe received data into displayed images, and the identified componentsmay be visually distinguished on a display 210. In some embodiments, thedisplay 210 may be included as a component of the imaging console 110(shown in FIG. 1). In other embodiments, the display 210 may be anindependently located device that communicates either wirelessly orthrough a wired connection with the imaging system 100. In someembodiments, the display 210 may be remotely located.

Based on the assessment of the scanned object or region of interest, thediagnostic logic 200 can be configured to generate a score indicatingthe health condition of a patient. For example, in one instance, if thescanned object of interest was a mass, a score of zero may indicate norisk of malignancy while a score of ten may indicate a high risk ofmalignancy. With this score, a physician may recommend a particulartreatment which may include monitoring, life-style changes, medication,radiation, and/or surgery. The score may also be helpful to convince apatient of the existence and gravity of their condition.

In one example, the imaging system 100 is configured to analyzeultrasound data collected from a scan of a carotid artery. In theexample shown in FIG. 5, the imaging system includes component partsthat are substantially similar to the component parts of the imagingsystem 100 shown in FIG. 1, except as otherwise described herein. Theimaging console 500 comprises an ultrasound imaging console and theimaging probe 505 comprises an ultrasound probe, which includes one ormore transducers 510 that acquire radio frequency data from the scannedobject 515, or carotid artery. The carotid arteries comprise a pair ofarteries that pass up through a patient's neck and supply blood to thehead. By identifying and characterizing plaques from carotid ultrasounddata, an assessment can be made as to a patient's risk of stroke orheart attack without an invasive procedure. The epidemiologic findingthat cardiovascular and cerebrovascular morbidity are well correlatedhas led to the recognition of possible surrogates for the costly andsometimes invasive evaluation of the coronary circulation. An example ofthis is the Intima-Media Thickness (IMT) measurement, which is arecognized non-invasive means of assessing subclinical atherosclerosis.

Illustrated in FIG. 5 is a simplified diagram of one embodiment of theultrasound probe 505 including a linear array of the transducers 510.Depending on the type of probe, different numbers of transducers may beused, for example, 192 transducers. The transducers 510 may pulseseparately, or together creating a plurality of the scan lines 520. Byplacing the probe 505 near or against a patient's neck, ultrasound datacan be collected from the carotid artery 515.

To perform a scan, the ultrasound probe 505 would be placed near oragainst the skin surface S of a patient's neck near the carotid arteryof interest 515. The transducers 510 of the probe would be pulsed alongscan lines 520 and then acquire echoes of backscatter signals 525 (notshown) reflected from the tissue along each scan line. Different typesand densities of tissue absorb and reflect the ultrasound pulsesdifferently. Tissues that receive the pulsed signal reflect and transmitsome of the pulse energy as a backscatter or reflected signal. Thebackscattered signals 525 are then received by the transducers 510 inthe probe 505. The difference between the signals transmitted andreceived by the probe 505 is that the received signal 525 is theattenuated and backscattered version of the transmitted signal 520. Thisbackscatter signal 525 is characteristic of the type of tissue thatreflected it. Differences in the backscatter signal 525 along each scanline 520 can be determined by performing a frequency analysis, usingspectral analysis and autoregressive coefficients, a waveletdecomposition, and/or a curvelet decomposition on the signals. As aresult, identifying different signal characteristics along each scanline allows for a correlation to the type of tissue associated withthose particular signal characteristics. As described above, signalcharacteristics of the backscattered signal 525 can serve as a signaturefor different types of components within an object, including, forexample, plaque components within an artery.

With reference again to FIG. 1, the data collected by the ultrasoundprobe 505 is initially in the form of raw radio frequency (RF) data 130of the backscattered signals along each scan line. The RF data 130 isthen analyzed to determine various signal characteristics that mayidentify associated tissue types. The signal analyzer logic 135 isconfigured to process and analyze the radio frequency data 130 toidentify, in real-time, the vascular components of the scanned carotidartery. Because different types and densities of tissue absorb andreflect the ultrasound pulses differently, the signal analyzer logic 135utilizes the reflected backscatter data 130 transmitted back to theprobe 505 to assemble a two-dimensional ultrasound characterization of ablood vessel from hundreds of pulse/acquisition cycles. In thisembodiment, the logic is configured to identify various types of plaquecomponents and to provide an assessment as to the patient's conditionbased on the type of plaque identified, an amount of plaque componentidentified, or both.

In one embodiment, the signal analyzer logic 135 includes logic totransform the RF data 130 to the frequency domain and analyze frequencyinformation of the signals to determine one or more signal properties140. For example, each scan line can be analyzed in segments and signalproperties are determined for each segment. The segments may be equal insize, different in size, equally spaced from each other, overlappingeach other, and/or defined in other desired ways.

Illustrated in FIG. 6 is one example of RF data of one scan line 600plotted as voltage over time. The scan line can be analyzed in segmentsrepresented by the windows illustrated in the figure, such as window605. The RF data within the window 605 is transformed in this embodimentto a power spectrum density plot as shown in FIG. 7. Signal propertiesfrom the segment 605 are determined from the power spectrum of FIG. 7.Signal properties, in this case also referred to as spectral properties,may include the y-intercept, maximum power, mid-band fit, minimum power,frequencies at maximum and minimum powers, slope of regression line,integrated backscatter, or combinations of these or derived from theautoregressive model or filter coefficients or image texture basedproperties or others and combinations of others.

With reference again to FIG. 1, the signal properties 140 are processedby the correlation logic 145 configured to correlate the signalproperties of the scan line segment with the type of vascular componenthaving those or similar signal properties. In that regard, thecorrelation logic 145 is configured to compare and match the signalproperties 140 to pre-determined tissue signal properties 150. In oneembodiment, the pre-determined signal tissue properties 150 areconfigured in a data structure that associates measured or observedsignal properties to a type of vascular component such as normal tissue,the lumen, and types of plaque components that may be present. Variousplaque components include calcifications, fibrous tissue, lipidic tissueor foam cells, and calcified-necrotic tissue. In another embodiment, thepre-determined signal tissue properties 150 are configured in a datastructure that associates measured or observed signal properties to atype of mass lesion component such as normal tissue, cancerous tissue,hyperplastic tissue, hypertrophic tissue, immune cells, and types oftumor cells that may be present. Various mass lesion components includemacrophages, calcium, fibrous, fibrolipid, and necrotic regions.

The correlation logic 145 matches the signal properties 140 from a scanline, or a region of the scan line, to the pre-determined properties150, factors in any relevant secondary parameters 160 and outputs atissue characterization 165 that identifies the type of tissue. Thesystem may then repeat the analysis for other segments on this scan lineand for the other scan lines. In some embodiments, the system 100 mayrepeat the analysis for the imaging data received by each of the otherimaging modalities. In some such embodiments, the system 100 utilizesthe correlation logic 145 to combine the correlation conclusions fromdifferent imaging modalities to arrive at a final tissuecharacterization 165.

In some embodiments, as shown in FIG. 4, once a sufficient amount ofultrasound data is analyzed and characterized, the diagnostic logic 200may be used to generate an assessment 205 as to the type and amount ofplaque identified and a health condition of the patient in terms ofcardiovascular disease or other associated health problems.Additionally, the diagnostic logic 200 may be configured to reconstructthe ultrasound data into displayed images and, the identified componentscan be visually distinguished on the display 210. Based on theassessment 205 of plaque composition, the logic 200 can be configured togenerate a score indicating the health condition of a patient. Forexample, a score of zero may indicate no risk of heart attack while ascore of ten may indicate a high risk of heart attack. With this score,a physician may recommend a particular treatment which may includemonitoring, life-style changes, medication and/or surgery. The score mayalso be helpful to convince a patient of their condition. As mentionedabove, while the example above is set forth in relation to use of anultrasound transducer, other forms of energy emitters such lasers orlight sources may be controlled to take advantage of the systems andmethods described above.

Illustrated in FIG. 8 is one embodiment of signal analyzer logic 800 forprocessing and analyzing radio frequency ultrasound data. It will beappreciated that the signal analyzer logic 800 may be embodied as partof an ultrasound imaging console or as part of a separate system thatreceives raw radio frequency data from an ultrasound console. If theradio frequency data is in analog form, a digitizer 805 may be providedto digitize the data. A signal processing logic 810 is configured toprocess each scan line of the ultrasound data and transform it to aformat that can be analyzed. To reduce processing time, border detectionlogic 815 may be used to determine the location of the borders of thevessel wall being scanned. Since the analysis is most interested in thecomponents of the carotid artery, scan line data outside of the arterycan be filtered and removed. One example of a border detection system isdescribed in U.S. Pat. No. 6,381,350, entitled “Intravascular UltrasonicAnalysis Using Active Contour Method and System,” which is incorporatedherein by reference for all purposes.

After border detection, or segmentation of the tissue of interest, thescan line data is transformed. Of course, border detection can beperformed after transformation. Transformation logic 820 is configuredto transform the remaining scan line data into a format suitable foranalysis. In general, the transformed format should match the sameformat used to build the pre-determined signal properties of thevascular component. In one embodiment, the transformation logic 820transforms the data to a power spectrum plot of frequency versus poweroutput as shown in FIG. 7. Various transformation algorithms include aFourier transformation, Welch periodograms, and auto-regressivemodeling. Other types of transformations can include transforming thedata to wavelets that provide an image with frequency and timeinformation. For example, other signal processing techniques may includewavelet decomposition or curvelet decomposition to deliver parametersthat are relevant for discrimination between tissue types while notbeing influenced by the system transfer function of the imaging systemand probe. Another transformation includes using impedance, rather thanfrequency, which gives an image of acoustic impedance. In this format,different tissue components have different impedance properties thatprovide different signal reflections. In the following example, a powerspectrum density plot is used from a Fourier transformation.

With further reference to FIG. 8, spectral analysis logic 825 analyzesthe power spectrum of the scan line data to determine its spectralproperties 830. As mentioned previously, spectral properties orparameters may include maximum power, frequency at the maximum power,minimum power, the frequency at the minimum power, the slope,y-intercept, mid-band fit, and integrated backscatter. The spectralparameters 830 are then inputted to a classification logic 835 thatattempts to classify the spectral parameters associated to a particularscan line segment with previously measured spectral parameters from aknown vascular component. As mentioned above, the signal analyzingtechniques need not be limited to spectral analysis and autoregressivecoefficients, but could entail use of wavelet decomposition or curveletdecomposition to deliver parameters that may be used by theclassification logic 835 to discriminate between tissue types.

In one embodiment, a classification data structure 840 contains astatistical classification of measured or observed spectral properties(and/or other properties) associated with particular types of vascularcomponents. The classification data structure 840, in one embodiment, ispreviously generated from laboratory studies that correlateintra-vascular ultrasound data analysis of tissue samples with theircorresponding histology sections. One example of this process isdescribed in U.S. Pat. No. 6,200,268 B1, entitled “Vascular PlaqueCharacterization,” issued Mar. 13, 2001, which is incorporated herein byreference for all purposes.

Referencing FIGS. 1 and 8, a variety of pattern recognition approachesmay be used by the classification logic 835 and/or correlation logic145. For example, the database 155 of relevant secondary parameters 160and pre-determined tissue signal properties 150 could be the startingpoint of various pattern recognition approaches, covering, but notlimited to, classification trees, random forests, neural networks,regression trees, principal components, and/or a combination of these,or others, to arrive at an accurate tissue characterization. Forexample, in one embodiment, the pre-determined tissue signal properties150 and/or the secondary parameters 160 may be stored in the database155 as a classification tree or a regression tree having branch nodeconditions based on the pre-determined tissue signal properties and oneor more leaf nodes that identify a tissue component. In anotherembodiment, the pre-determined tissue signal properties 150 may beembodied in the database as an artificial neural network having one ormore nodes that identify a tissue component. In some embodiments, theclassification logic 835 and/or the correlation logic 145 may utilize arandom forest classifier to analyze a number of classification trees(e.g., different classification trees based on different pre-determinedsignal properties or based on a multitude of different imagingmodalities) to arrive at a tissue characterization.

An example of a statistical classification tree 900 is shown in FIG. 9.The tree 900 may be based on a number of spectral properties measuredfrom ultrasound data and matched to tissue components from correspondinghistology samples. A variety of statistical software applications may beused to compile the data such as S Plus by Statistical Sciences, Inc.,Seattle, Wash. The tree 900 includes a root node 905 that branches basedon the signal properties compiled from the statistical algorithm, andcould also include other parameters relevant to patient demographics.For example, the first branch level is based on a value of mid-band fit.The tree 900 terminates at leaf nodes (shown as boxes) that represent aparticular type of tissue. In this example, the leaf nodes indicatetissue type C (calcified), type F (fibrous), type FL (fibro-lipidic) andtype CN (calcified necrosis). Thus, by inputting a set of signalproperties, the classification tree 900 can be traversed in accordancewith the branching conditions and lead to a leaf node that identifiesthe type of tissue matching the inputted signal properties.

In a simple example, suppose spectral properties from one segment of abackscatter signal are determined to be: mid-band fit=−11.0 and minimumpower=−8.2. Processing these properties through the classification tree900 causes the tree to be traversed in two levels and end at a leaf node910. Reaching leaf node 910 indicates that the segment corresponds to aType=C (calcified) plaque. In this case, other spectral properties arenot necessary to identify the tissue because statistical data from themeasured histology samples show that some calcified plaque tissues had amid-band fit>−11.4 and a minimum power>−8.6 as spectral properties. Ofcourse, these are examples of spectral properties and the values maychange based on the amount and type of data collected, the statisticalalgorithm used, or other factors that may affect the results.

Continuing the analysis for other segments of a backscatter signal andsegments from other scan lines collected from a scan, the system canprovide helpful identification of the types of components within thecarotid artery. Additionally, based on the location of a segment along ascan line, the system can make a determination as to the location of thecorresponding tissue within the carotid artery. Then by combining datafrom adjacent segments and adjacent scan lines having the same tissuecomponent, the system can estimate the size and/or volume of the tissuecomponent. This may be important because certain components may create agreater risk of plaque rupture and/or vessel occlusion based on theirlocation and/or size and it would be helpful to identify theseconditions. Similarly, in the evaluation of other conditions, usingultrasound or other imaging modalities, certain components may indicatea greater likelihood of pathology than others.

Illustrated in FIG. 10 is one embodiment of a methodology 950 associatedwith analyzing ultrasound signals and identifying the type of tissuecomponent that corresponds to the signals. The illustrated elementsdenote “processing blocks” and represent computer software instructionsor groups of instructions that cause a computer or processor to performan action(s) and/or to make decisions. Alternatively, the processingblocks may represent functions and/or actions performed by functionallyequivalent circuits such as a digital signal processor circuit, anapplication specific integrated circuit (ASIC), or other logic device.The diagram does not depict syntax of any particular programminglanguage. Rather, the diagram illustrates functional information oneskilled in the art could use to fabricate circuits, generate computersoftware, or use a combination of hardware and software to perform theillustrated processing. It will be appreciated that electronic andsoftware applications may involve dynamic and flexible processes suchthat the illustrated blocks can be performed in other sequencesdifferent than the one shown and/or blocks may be combined or separatedinto multiple components. They may also be implemented using variousprogramming approaches such as machine language, procedural,object-oriented, artificial intelligence, or other techniques. Thisapplies to all methodologies described herein.

In other embodiments, the steps of the methodology 950 may be employedto analyze imaging signals received from another imaging modality and toidentify the type of tissue component that corresponds to the signals.In such embodiments, instead of receiving ultrasound data and analyzingultrasound imaging properties such as spectral properties, the imagingsystem 100 may instead receive imaging data specific to the particulartype of imaging modality and analyze imaging properties specificallyassociated with the type of imaging modality used, in light of therelevant secondary parameters.

With reference to FIG. 10, analysis may begin as ultrasound data isreceived in real time during a scan or after a scan is completed (block955). For example, in one embodiment, the region of interest for thescan is a vascular object such as a carotid artery. If the ultrasounddata is still in the raw radio frequency form, it is digitized (block960). In one embodiment, the digitized data is analyzed along a scanline, in one or more segments. The embodiment of FIG. 10 illustrates theanalysis of one segment of data. Although not shown in FIG. 10, theprocessing repeats for each segment of a scan line and repeats for otherscan lines until complete or until processing is stopped. Optionally,the process may allow for changing the properties of how a scan line issegmented such as defining various sizes and intervals of segments.

For a scan line being analyzed, a segmentation algorithm may be used toidentify the borders of the target object (block 965) and the analysiscan be focused on the scan line data corresponding to the target object.Since the scan is not intravascular in this example, a scan line thatpasses through the target object may pass through two or more walls ofthe object. For example, FIG. 5 illustrates a number of scan lines 520passing through two walls of a target or scanned object 515. Thus, theborder detection would attempt to search and identify at least twoborders along a scan line. Many different segmentation methods areavailable including analyzing signal properties of the scan line,reconstructing an image from the ultrasound data and detecting bordersfrom the image data, and other methods. Scan line data outside theborders of the target object may be ignored or removed from analysis ifdesired.

With reference again to FIG. 10, the scan line can be segmented andanalyzed by segment. In one embodiment, the signal data from a segmentis transformed to a power spectrum form (block 970) such as in FIG. 7.Spectral properties may be determined from the power spectrum (block975) which may include the y-intercept, maximum power, mid-band fit,minimum power, frequencies at maximum and minimum powers, slope ofregression line, integrated backscatter, and/or other properties fromthe power spectrum. Other properties may be determined from waveletdecomposition, curvelet decomposition techniques, filter coefficients,or principal component analyses. The spectral properties and/or otherproperties of the scan line data are then compared to correspondingpre-determined properties of known tissue components to determine whichtype of component best matches the scan line signal properties.

In one embodiment, the pre-determined tissue properties are structuredas a classification tree generated from statistical analysis of how theproperties correlate to a type of tissue component. An example of a treestructure for analysis of spectral properties is shown in FIG. 9, whichincludes branch nodes having conditions for spectral property values.The scan line spectral properties are then processed through the tree(block 980), traversing branches based on how the spectral propertiesmeet the conditions of the branch nodes. The tree is traversed to a leafnode that identifies a type of tissue component. The spectral propertiesof the scan line segment are then characterized as this type ofcomponent (block 985).

The analysis continues for the other segments of the scan line and otherscan lines. When a sufficient amount of scan line data has beencharacterized, an assessment can be generated and outputted reflecting ahealth condition of the patient and/or condition of the blood vessel(block 990). A diagnostic score and/or an image may also be generatedindicating the health condition which may include a display of theimaged tissue and any associated pathology. For example, in oneembodiment, the displayed image may indicate the type and amount ofplaque identified, the location of the plaque, the potential risk ofheart attack, or other conditions. In a situation where the user hasimaged the carotid artery, by determining the condition of the carotidartery through a non-invasive scan, an assessment can be made as to apatient's cardiovascular condition. A presumption made is that there isa correlation between the condition of the carotid arteries and thecondition of the coronary arteries. If the carotid arteries show certainlevels of plaque, it can be presumed that similar conditions may existin the coronaries. In another embodiment, the displayed image mayindicate the type and amount of cancerous tissue identified, thelocation of the tumor, the potential risk of invasive cancer, or otherconditions.

Other secondary factors may also be used to provide a diagnosis such asa patient's demographics, medical history, family medical history,previous treatment history, and other factors. An appropriate treatmentmay then be prescribed. External scanning of the patient's tissues ofinterest allows for a diagnosis without having to perform an invasiveprocedure that may expose a patient to the risks associated with biopsyand/or surgery.

In one embodiment, the system can be configured to identify tissue froman external breast scan. For example, scanning an unknown lump within abreast and determining whether it may be cancerous can serve as an earlydiagnostic tool. In this embodiment, pre-determined correlations betweentissue signal properties and cancerous and non-cancerous tissue would beobtained and stored in the database or data structure 155 (shown in FIG.1). The correlations can be obtained in a similar manner as thosedescribed above. This may include collecting imaging data (i.e., imagingdata corresponding to a variety of different imaging modalities) fromphysical samples of tissue and matching the data with correspondingtissue from a histology sample of the tissue.

Such an embodiment can be implemented where the pre-determined tissuesignal properties 150 within the data structure 155 would include signalproperties of cancerous tissue, non-cancerous tissue, or both (asopposed to pre-determined plaque properties, for example). One form ofthe database 155 may be a statistical classification tree. To perform ascan, a non-invasive probe would be positioned against the tissue of abreast near a region of interest (e.g., a suspected lump). The receiveddata (e.g., reflected imaging data) would then be analyzed to determinespectral properties or other signal properties in a similar manner asdescribed previously. If the signal properties sufficiently matchpre-determined tissue properties of cancerous tissue, the system canoutput a signal indicating that cancerous tissue may be present. Sincethe type of cancerous tissue is not as important for this type of earlydiagnosis, a simple YES or NO can be the output. It will be appreciatedthat the system can be configured to identify any desired type of tissueor object using the techniques discussed here.

Suitable software for implementing the various components of the presentsystem and method using the teachings presented here include programminglanguages and tools such as Java, Pascal, C#, C++, C, CGI, Perl, SQL,APIs, SDKs, assembly, firmware, microcode, and/or other languages andtools. The components embodied as software include readable/executableinstructions that cause one or more computers, processors and/or otherelectronic device to behave in a prescribed manner. Any software,whether an entire system or a component of a system, may be embodied asan article of manufacture and maintained as part of a computer-readablemedium as defined previously. Another form of the software may includesignals that transmit program code of the software to a recipient over anetwork or other communication medium. It will be appreciated thatcomponents described herein may be implemented as separate components ormay be combined together.

The systems and methods described herein may be used in a variety ofnon-invasive imaging applications, including without limitation:non-invasive carotid artery and plaque imaging (resulting incharacterization of plaque components as opposed to merely plaquelocation or thickness), non-invasive myocardium imaging, non-invasivebreast cancer detection (and staging), image guidance for biopsies(i.e., needle guidance as well as for identifying cancerous versusnon-cancerous tissue for biopsy tissue extraction), and non-invasivedetection and/or staging of other cancers (e.g., skin cancer, oralcancer, other natural orifice cancer imaging). In some embodiments, thesystem shown in FIG. 1 and the method described in FIG. 10 may be usedto characterize tissue to diagnose various disease states, such as, byway of non-limiting example, liver cirrhosis, kidney or gallbladderstones, ischemic myocardium, the contents of pleural effusions, bowelobstructions, joint effusions, etc. In each of these embodiments, thedatabase 155 would be configured to contain pre-determined tissueimaging properties 150 and secondary parameters 160 associated with theparticular types of tissue found in such conditions. The imaging systemcould utilize this database 155 to compare and correlate the signalproperties of the tissue-of-interest with the pre-determined properties150 to accurately characterize the tissue.

The systems and methods described herein provide automated, reliable,and reproducible tissue characterization using a non-invasive imagingprobe and a database containing pre-determined tissue properties forvarious types of tissue components, thus reducing the need for highlytrained, highly experienced observers and generally eliminating observerbias (as well as intra- and inter-observer variability). In addition,the systems and methods described herein may allow for a diagnosis fromthe tissue characterization in real-time, thereby reducing the need fortissue biopsies and the time necessary to receive a diagnosis from thebiopsy results. Even in instances where a biopsy is still indicatedafter tissue characterization by the systems and methods disclosedherein, the systems and methods described above may be utilized toprovide detailed image guidance for biopsies (i.e., directing the userto a particular tissue type or margin for biopsy). The interpretation ofbi-angle or multi-angle imaging data of a target tissue offers theability to non-invasively distinguish between anisotropic and isotropictissues. Moreover, the systems and methods disclosed herein offer theability to analyze multiple parameters, such as, by way of non-limitingexample, a patient's pre-existing medical condition, the angles ofincidence of the imaging, and/or the imaging data from multiple imagingmodalities to optimize the final tissue characterization.

While the present invention has been illustrated by the description ofembodiments thereof, and while the embodiments have been described inconsiderable detail, it is not the intention of the applicants torestrict or in any way limit the scope of the appended claims to suchdetail. Additional advantages and modifications will readily appear tothose skilled in the art. Therefore, the invention, in its broaderaspects, is not limited to the specific details, the representativeapparatus, and illustrative examples shown and described. Accordingly,departures may be made from such details without departing from thespirit or scope of the applicant's general inventive concept.

Persons of ordinary skill in the art will appreciate that theembodiments encompassed by the present disclosure are not limited to theparticular exemplary embodiments described above. In that regard,although illustrative embodiments have been shown and described, a widerange of modification, change, and substitution is contemplated in theforegoing disclosure. It is understood that such variations may be madeto the foregoing without departing from the scope of the presentdisclosure. Accordingly, it is appropriate that the appended claims beconstrued broadly and in a manner consistent with the presentdisclosure.

What is claimed is:
 1. A system for determining tissue composition, thesystem comprising: an imaging system with a non-invasive probe withactive imaging components for emitting energy and collecting imagingdata including reflected signals from an object of interest; a signalanalyzer for analyzing the imaging data and determining one or moresignal properties from the reflected signals; and a correlationprocessor configured to associate the one or more signal properties topre-determined tissue signal properties of different tissue componentsthrough a pattern recognition technique wherein the pre-determinedtissue signal properties are embodied in a database, the correlationprocessor further configured to identify a tissue component of theobject based on the pattern recognition technique.
 2. The system ofclaim 1, wherein the imaging system collects imaging data from more thanone imaging modality, the signal analyzer is configured to analyze morethan one type of imaging data to determine signal properties associatedwith each imaging modality, and the correlation processor is configuredto identify a tissue component of the object based on associating thesignal properties to pre-determined tissue signal properties ofdifferent tissue components for the different imaging modalities.
 3. Thesystem of claim 2, wherein the probe includes more than one imagingmodality and is configured to collect imaging data via more than oneimaging modality.
 4. The system of claim 1, wherein the pre-determinedtissue signal properties are embodied in the database as aclassification tree having branch node conditions based on thepre-determined tissue signal properties and one or more leaf nodes thatidentify a tissue component.
 5. The system of claim 4, wherein thecorrelation processor is configured to traverse the classification treeto a leaf node based on the pattern recognition technique of comparingthe one or more signal properties from the reflected signals to thebranch node conditions.
 6. The system of claim 1, wherein thepre-determined tissue signal properties are embodied in the database asan artificial neural network having one or more nodes that identify atissue component.
 7. The system of claim 1, wherein the pre-determinedtissue signal properties are embodied in the database as a regressiontree having branch node conditions based on the pre-determined tissuesignal properties and one or more leaf nodes that identify a tissuecomponent.
 8. The system of claim 1, wherein the pattern recognitiontechnique used by the correlation processor includes a random forestclassifier.
 9. The system of claim 1, wherein the one or more signalproperties includes at least one of: one or more spectral properties,one or more wavelet decomposition properties, one or more curveletdecomposition properties, and one or more filter coefficients.
 10. Thesystem of claim 1, further including secondary parameters embodied inthe database, the secondary parameters each associated with particularpre-determined tissue signal properties, wherein the correlationprocessor uses the secondary parameters to more accurately associate thesignal properties to the pre-determined tissue signal propertiesassociated with the secondary parameters.
 11. The system of claim 10,wherein the secondary parameters comprise at least one of: a body-massindex of the patient, an angle of incidence of the active imagingcomponents of the probe relative to a longitudinal axis of the object, afrequency employed by the active imaging components of the probe, apre-existing health condition of the patient.
 12. The system of claim 1,further including segmentation logic configured to determine borders ofthe object from the reflected signals.
 13. The system of claim 12,wherein the signal analyzer logic selectively analyzes substantiallyonly the imaging data within the borders of the object.
 14. The systemof claim 1, wherein the non-invasive probe is configured to collectultrasound data with a plurality of transducers.
 15. The system of claim1, wherein the imaging system further includes a diagnostic logic forgenerating an assessment as to a health condition based on the tissuecomponents of the object identified.
 16. A method of identifying one ormore tissue components of a scanned object of a patient, the methodcomprising: receiving reflected signals from a non-invasive probescanning the object from a location external to the object; determiningone or more signal properties from the reflected signals; associatingthe one or more signal properties to pre-determined signal properties oftissue components of an object similar to the scanned object wherein thepre-determined signal properties comprise classification conditionsstored in a data structure; and identifying one or more tissuecomponents based on the associating.
 17. The method of claim 16, whereinthe pre-determined signal properties comprise branch node conditionsbased on the pre-determined signal properties and one or more leaf nodesidentifying a type of tissue component.
 18. The method of claim 16,wherein the reflected signals include a plurality of backscattered scanlines and the determining step includes determining signal propertiesfor a plurality of segments from the plurality of scan lines.
 19. Themethod of claim 16, wherein the receiving step includes collectingreflected signals by a plurality of imaging modalities.
 20. The methodof claim 16, wherein associating the one or more signal properties topre-determined signal properties of tissue components of an objectsimilar to the scanned object includes selecting the pre-determinedtissue properties based on secondary parameters associated with theobject.
 21. The method of claim 20, wherein the receiving step includesreceiving reflected signals from the non-invasive probe scanning theobject from more than one angle of incidence relative to a longitudinalaxis of the object, and wherein the angles of incidence comprisesecondary parameters.
 22. The method of claim 20, wherein the secondaryparameters comprise at least one of: a body-mass index of the patient,an angle of incidence of the active imaging components of the proberelative to a longitudinal axis of the object, a frequency employed bythe active imaging components of the probe, a pre-existing healthcondition of the patient.